• DocumentCode
    2071386
  • Title

    Graph-based methods for Horn knowledge compression

  • Author

    Hammer, Peter L. ; Kogan, Alexander

  • Author_Institution
    RUTCOR, Rutgers Univ., New Brunswick, NJ, USA
  • Volume
    3
  • fYear
    1994
  • fDate
    4-7 Jan. 1994
  • Firstpage
    300
  • Lastpage
    309
  • Abstract
    Horn knowledge bases are widely used in many applications. The paper is concerned with the optimal compression of propositional Horn production rule bases/spl minus/one of the most important knowledge bases used in practice. The problem of knowledge compression is interpreted as a problem of Boolean function minimization. It was proved by P.L. Hammer and A. Kogan (1993) that the minimization of Horn functions, i.e. Boolean functions associated to Horn knowledge bases, is NP-complete. The paper deals with the minimization of quasi-acyclic Horn functions, the class of which properly includes the two practically significant classes of quadratic and of acyclic functions. A procedure is developed for recognizing in quadratic time the quasi-acyclicity of a function given by a Horn CNF, and a graph-based algorithm is proposed for the quadratic time minimization of quasi-acyclic Horn functions.<>
  • Keywords
    Boolean functions; Horn clauses; data compression; graph theory; knowledge based systems; minimisation; Boolean function minimization; Horn CNF; Horn knowledge bases; Horn knowledge compression; NP-complete; acyclic functions; conjunctive normal form; graph-based algorithm; graph-based methods; knowledge compression; optimal compression; propositional Horn production rule bases; quadratic time minimization; quasi-acyclic Horn functions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on
  • Conference_Location
    Wailea, HI, USA
  • Print_ISBN
    0-8186-5090-7
  • Type

    conf

  • DOI
    10.1109/HICSS.1994.323341
  • Filename
    323341